deML

deML implements a maximum-likelihood demultiplexing algorithm that assigns Illumina sequencing reads to samples by evaluating the probability that an observed index sequence originated from each sample.

Key Features:

• Maximum Likelihood Algorithm: Computes the likelihood that an observed index sequence derives from each sample instead of relying on a fixed mismatch count.
• Quality Score Generation: Produces a per-read quality score that reflects the probability of correct sample assignment.
• Handling Poor Sequencing Quality: Uses the likelihood framework to accommodate variations and errors in index sequencing quality, reducing misassignments.
• Error Threshold Setting: Enables filtering of assignments based on user-specified error thresholds derived from the generated quality scores.

Scientific Applications:

• Pooled high-throughput Illumina sequencing: Demultiplexes reads from multiplexed libraries to recover sample-specific data.
• Genomic studies: Assigns sequence reads to samples in population, resequencing, and other genomic workflows requiring precise sample identification.
• Transcriptomics and large-scale experiments: Separates multiplexed RNA-seq and other sequencing datasets where accurate sample demultiplexing is critical.

Methodology:

Calculates likelihoods of each read's index sequence originating from each sample while modeling sequencing errors in index reads, generates per-read assignment quality scores, and applies error-threshold filtering based on those scores.